1. Field of the Invention
The disclosed systems and methods relate to automatically determining a state of a system user. Particularly, in one claimed embodiment, the methods relate to determining a state of the user based on modeling the user state with physiological and self-reported measures.
2. Background
The ability to develop algorithms that estimate human user states in real-time settings based on physiological inputs has been limited. Almost exclusively, scientists have relied on machine learning techniques to train these algorithms to find optimal weights among a data set that best match a particular measure of human user state. However, standard machine learning practices have produced user state estimation algorithms that suffer from low resolution (e.g., low vs high workload), infrequent updates (e.g., once per minute), and poor generalizability across human operators. There is no standard defined process that sufficiently overcomes these limitations through a single approach. Variations of machine learning practices, such as artificial neural networks (ANN) and support vector machines (SVM), have been used across scientific communities in an attempt to create user state estimation algorithms. Some scientists have attempted to produce high output resolution for user state estimation algorithms by creating a laboratory based task environment that varies a human operator's functional state (e.g., workload) across many levels, collecting self-reported metrics for the state of interest within each level, and training a model that identifies the optimal relationship between feature inputs and the user state. While this approach carries merit, the number of levels that are practical to create and present to a human operator in a data collection session is very limited within reasonable time constraints. Furthermore, scientists typically make the assumption that a self-reported estimate of user state remains constant through a data collection trial, when it is highly likely there is variability in the operator's state throughout each trial. The ability to produce frequent real-time updates has also been limited because of the large time window of physiological data required to reliably detect a human operator's state. The most common practice has been to simply accept this limitation by scaling back the user state estimation update rate to one that produces acceptable accuracy.